Contextualized visual analytics for multivariate events
For event analysis, the information from both before and after the event can be crucial in certain scenarios. By incorporating a contextualized perspective in event analysis, analysts can gain deeper insights from the events. We propose a contextualized visual analysis framework which enables the identification and interpretation of temporal patterns within and across multivariate events. The framework consists of a design of visual representation for multivariate event contexts, a data processing workflow to support the visualization, and a context-centered visual analysis system to facilitate the interactive exploration of temporal patterns. To demonstrate the applicability and effectiveness of our framework, we present case studies using real-world datasets from two different domains and an expert study conducted with experienced data analysts.
Authors
- Zhong-Qin Lin
- Gennady Andrienko (ORCID: https://orcid.org/0000-0002-8574-6295)
- Lei Peng
- Natalia Andrienko (ORCID: https://orcid.org/0000-0003-3313-1560)
- Siming Chen (ORCID: https://orcid.org/0000-0002-2690-3588)
Institutions
- City, University of London (GB)
- Fudan University (CN)
- Fraunhofer Institute for Intelligent Analysis and Information Systems (DE)
Publication Details
- Journal
- Visual Informatics
- Published
- 2025-03-22
- DOI
- https://doi.org/10.1016/j.visinf.2025.100234
- Citations
- 4
- Primary Topic
- Data Visualization and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 5.10
Funders
- National Natural Science Foundation of China
- Bundesministerium für Bildung und Forschung